⚡️ Speed up function funcA by 6%

[codeflash-ai]

📄 6% (0.06x) speedup for funcA in code_to_optimize/code_directories/simple_tracer_e2e/workload.py

⏱️ Runtime : 1.06 milliseconds → 998 microseconds (best of 402 runs)

📝 Explanation and details

Here's an optimized rewrite of your program, aiming at faster runtime for " ".join(map(str, range(number))), which is the true hotspot in your profiling. The approach is to avoid creating a list of strings, which map(str, range(...)) produces lazily but then realizes on join. By using a more efficient batch conversion with a generator, or more efficiently, using string multiplication and concatenation to minimize intermediate allocations, we can get further speedup, but for this joining str(int) is already quite optimal in CPython.

However, if performance is even more important (especially for large number), using a precomputed buffer or using f-strings with generator expressions (which CPython optimizes well internally) sometimes shaves a bit off the time compared to map(). Also, since number is at most 1000, looping isn't such a big deal, but the one possible vector for speedup is.

• Use list comprehension with direct unpacking and " ".join(...), which sometimes benchmarks very slightly faster than map(str, ...) in CPython for small numbers due to reduced indirection.
• Precompute small strings via a lookup table (for even more repeated cases), but here that's likely overkill.
• Remove all unused calculations (k and j), since you only return the string.

Final optimized version.

Notes

• You may see a 10-15% boost over map(str, ...) join for short ranges in current CPython.
• If this wasn't returning, but writing to a file or needing the output for streaming, a generator version (yield from) or a manual buffer with io.StringIO may be faster still.
• Using " ".join(map(str, ...)) is already a CPython C-optimized path, so further speedup is minor and may not show for small N.
• All your intermediate variables (k, j) are computed but unused, so they are now removed to save CPU and memory.

---

If you must keep the unused variables for some side effect or requirement, use the code below (but it's less memory efficient).

But otherwise, the first form is as fast as you'll get for this.

Summary:

• Remove unnecessary variables if unused.
• Use list comprehension for slightly better performance on short ranges.
• For extremely high performance on larger numbers, consider using a buffer or io.StringIO if you need to scale beyond 1000.

Let me know if you'd like a version using advanced buffer tricks or for Cython!

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 42 Passed
⏪ Replay Tests	✅ 3 Passed
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

🌀 Generated Regression Tests and Runtime

import pytest  # used for our unit tests
from workload import funcA

# unit tests

# 1. Basic Test Cases

def test_funcA_zero():
    # Test with input 0, should return empty string (no numbers)
    codeflash_output = funcA(0) # 2.27μs -> 2.02μs (12.5% faster)

def test_funcA_one():
    # Test with input 1, should return "0"
    codeflash_output = funcA(1) # 2.54μs -> 2.42μs (4.54% faster)

def test_funcA_small_number():
    # Test with input 5, should return "0 1 2 3 4"
    codeflash_output = funcA(5) # 2.98μs -> 2.98μs (0.302% slower)

def test_funcA_typical_number():
    # Test with input 10, should return numbers 0 through 9 separated by spaces
    codeflash_output = funcA(10) # 3.31μs -> 3.29μs (0.609% faster)

# 2. Edge Test Cases

def test_funcA_negative_number():
    # Negative input should act as range(negative) which is empty, so return ""
    codeflash_output = funcA(-5) # 2.11μs -> 1.97μs (7.09% faster)

def test_funcA_large_number_limit():
    # Input above 1000 should be capped at 1000, so should return "0 1 ... 999"
    codeflash_output = funcA(1500); result = codeflash_output # 78.9μs -> 74.0μs (6.51% faster)
    # Should have 1000 numbers, separated by spaces
    nums = result.split()

def test_funcA_at_limit():
    # Input exactly 1000, should return "0 1 ... 999"
    codeflash_output = funcA(1000); result = codeflash_output # 77.1μs -> 72.3μs (6.68% faster)
    nums = result.split()

def test_funcA_just_below_limit():
    # Input 999, should return "0 1 ... 998"
    codeflash_output = funcA(999); result = codeflash_output # 76.9μs -> 71.9μs (6.95% faster)
    nums = result.split()

def test_funcA_float_input():
    # If input is float, should raise TypeError as range expects int
    with pytest.raises(TypeError):
        funcA(5.5)

def test_funcA_string_input():
    # If input is string, should raise TypeError as range expects int
    with pytest.raises(TypeError):
        funcA("10")

def test_funcA_none_input():
    # If input is None, should raise TypeError
    with pytest.raises(TypeError):
        funcA(None)

def test_funcA_boolean_input():
    # Input True should be treated as 1, so should return "0"
    codeflash_output = funcA(True) # 2.98μs -> 2.75μs (7.99% faster)
    # Input False should be treated as 0, so should return ""
    codeflash_output = funcA(False) # 1.36μs -> 1.23μs (10.5% faster)

# 3. Large Scale Test Cases

def test_funcA_large_scale_500():
    # Test with 500, should return "0 1 ... 499"
    codeflash_output = funcA(500); result = codeflash_output # 40.6μs -> 38.4μs (5.96% faster)
    nums = result.split()
    # Check that all numbers are present and in order
    for i, num in enumerate(nums):
        pass

def test_funcA_large_scale_999():
    # Test with 999, should return "0 1 ... 998"
    codeflash_output = funcA(999); result = codeflash_output # 78.7μs -> 74.1μs (6.23% faster)
    nums = result.split()

def test_funcA_large_scale_1000():
    # Test with 1000, should return "0 1 ... 999"
    codeflash_output = funcA(1000); result = codeflash_output # 77.2μs -> 71.6μs (7.87% faster)
    nums = result.split()

def test_funcA_large_scale_above_1000():
    # Test with 2000, should be capped at 1000, so "0 1 ... 999"
    codeflash_output = funcA(2000); result = codeflash_output # 76.7μs -> 72.1μs (6.41% faster)
    nums = result.split()

# Additional edge: input is exactly at int min/max (Python int is unbounded but test negative/large)
def test_funcA_large_negative():
    # Very large negative input should return ""
    codeflash_output = funcA(-1000000) # 2.35μs -> 2.21μs (6.80% faster)

def test_funcA_large_positive():
    # Very large positive input should be capped at 1000
    codeflash_output = funcA(1000000); result = codeflash_output # 77.7μs -> 72.5μs (7.23% faster)
    nums = result.split()
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

import pytest  # used for our unit tests
from workload import funcA

# unit tests

# ------------------------
# Basic Test Cases
# ------------------------

def test_funcA_zero():
    # Test with input 0: should return empty string
    codeflash_output = funcA(0) # 2.07μs -> 1.89μs (9.51% faster)

def test_funcA_one():
    # Test with input 1: should return "0"
    codeflash_output = funcA(1) # 2.52μs -> 2.38μs (5.87% faster)

def test_funcA_small_number():
    # Test with small input
    codeflash_output = funcA(3) # 2.81μs -> 2.77μs (1.44% faster)
    codeflash_output = funcA(5) # 1.50μs -> 1.63μs (7.96% slower)

def test_funcA_typical_number():
    # Test with a typical mid-range input
    expected = " ".join(str(i) for i in range(10))
    codeflash_output = funcA(10) # 2.71μs -> 2.61μs (3.86% faster)

# ------------------------
# Edge Test Cases
# ------------------------

def test_funcA_negative_number():
    # Test with negative input: should return empty string
    codeflash_output = funcA(-1) # 2.13μs -> 1.99μs (7.07% faster)
    codeflash_output = funcA(-100) # 1.16μs -> 1.11μs (4.50% faster)

def test_funcA_large_number_cap():
    # Test with input greater than 1000: should cap at 1000
    codeflash_output = funcA(1500); result = codeflash_output # 78.0μs -> 73.5μs (6.08% faster)
    # Should be numbers 0 to 999 (1000 numbers)
    expected = " ".join(str(i) for i in range(1000))

def test_funcA_exactly_1000():
    # Test with input exactly at cap: should return 0..999
    codeflash_output = funcA(1000); result = codeflash_output # 77.6μs -> 72.6μs (6.89% faster)
    expected = " ".join(str(i) for i in range(1000))

def test_funcA_input_is_string():
    # Test with string input (should raise TypeError)
    with pytest.raises(TypeError):
        funcA("10")

def test_funcA_input_is_float():
    # Test with float input (should raise TypeError)
    with pytest.raises(TypeError):
        funcA(5.5)

def test_funcA_input_is_none():
    # Test with None as input (should raise TypeError)
    with pytest.raises(TypeError):
        funcA(None)

def test_funcA_input_is_bool():
    # Test with boolean input (should treat as 0 or 1)
    codeflash_output = funcA(True) # 2.94μs -> 2.81μs (4.26% faster)
    codeflash_output = funcA(False) # 1.45μs -> 1.21μs (19.9% faster)

def test_funcA_input_is_list():
    # Test with list input (should raise TypeError)
    with pytest.raises(TypeError):
        funcA([5])

# ------------------------
# Large Scale Test Cases
# ------------------------

def test_funcA_large_scale_999():
    # Test with large input just below cap
    codeflash_output = funcA(999); result = codeflash_output # 78.6μs -> 75.0μs (4.71% faster)
    expected = " ".join(str(i) for i in range(999))

def test_funcA_large_scale_performance():
    # Test performance and correctness for the cap
    n = 1000
    codeflash_output = funcA(n); result = codeflash_output # 77.0μs -> 73.4μs (4.94% faster)
    # Split into list and check length
    nums = result.split()
    # Check all elements are consecutive integers as strings
    for idx, val in enumerate(nums):
        pass

# ------------------------
# Additional Edge Cases
# ------------------------

def test_funcA_input_is_zero_string():
    # Test with string "0" (should raise TypeError)
    with pytest.raises(TypeError):
        funcA("0")

def test_funcA_input_is_large_negative():
    # Test with large negative number
    codeflash_output = funcA(-99999) # 2.46μs -> 2.38μs (2.94% faster)

def test_funcA_input_is_very_large():
    # Test with very large input (should cap at 1000)
    codeflash_output = funcA(10**6); result = codeflash_output # 78.3μs -> 73.3μs (6.71% faster)
    expected = " ".join(str(i) for i in range(1000))

def test_funcA_input_is_object():
    # Test with object input (should raise TypeError)
    class Dummy: pass
    with pytest.raises(TypeError):
        funcA(Dummy())

def test_funcA_input_is_bytes():
    # Test with bytes input (should raise TypeError)
    with pytest.raises(TypeError):
        funcA(b"10")

def test_funcA_input_is_complex():
    # Test with complex input (should raise TypeError)
    with pytest.raises(TypeError):
        funcA(2+3j)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

To edit these changes git checkout codeflash/optimize-funcA-mcdqgzh8 and push.